In the operation of a many sensing systems intended to monitor objects of interest to an operator, it is generally desired to determine a collection plan and/or a collection schedule. The collection plan and/or schedule should satisfy various constraints such as physics constraints, occultation avoidance constraints, operational constraints, and collection value constraints, while also maximizing or minimizing a given payoff function, for example profit, number of images collected, quality of images collected, etc. Collection schedules are generally derived from a set of customer requests or provided to an operator from some other appropriate source. Further, when the sensor is mobile and dependent on supporting systems for orientation, for example an imaging package on a satellite, any potential collection schedules are strictly limited by the underlying abilities of the supporting systems themselves. This generates a series of constraints that every collection plan must observe enroute to maximizing payoff.
Typically this combined problem is solved by decomposing the problem into separate problems of planning and scheduling. Generally, a solution to the scheduling subproblem generates an “optimal” sequence (i.e., a walk) for a given payoff function (e.g., profit), and this scheduling sequence is then used to create a plan by testing the feasibility of the sequence against existing constraints by simulating the entire multi-point trajectory using a high fidelity simulator. If the test fails, the entire process or parts of the process are repeated (usually via an operator in the loop) until a feasible solution is obtained. This type of two-step analysis generally requires many iterations and is typically computationally expensive, particularly at the second step which typically involves Monte Carlo or other sampling type evaluations.
It would be advantageous to avoid this multi-step process by providing a method and apparatus which intergrates the entire problem as a dynamic optimization problem. This would avoid many of the simplifications and assumptions made in the current state-of-the-art in favor of a higher fidelity solution that enhances the payoff and enables solutions to be obtained more rapidly than by combinatorial techniques currently utilized in the art.
These and other objects, aspects, and advantages of the present disclosure will become better understood with reference to the accompanying description and claims.